{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n",
      "y_val:  (1000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_train:  (49000, 3, 32, 32)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch normalization: Forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:  [ 27.18502186  34.21455511  37.68611762]\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  mean:  [  4.44089210e-17   7.88258347e-17  -1.29757316e-17]\n",
      "  std:  [ 0.99999999  1.          1.        ]\n",
      "After batch normalization (nontrivial gamma, beta)\n",
      "  means:  [ 11.  12.  13.]\n",
      "  stds:  [ 0.99999999  1.99999999  2.99999999]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization\n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print('  means: ', a.mean(axis=0))\n",
    "print('  stds: ', a.std(axis=0))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'})\n",
    "print('  mean: ', a_norm.mean(axis=0))\n",
    "print('  std: ', a_norm.std(axis=0))\n",
    "\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print('After batch normalization (nontrivial gamma, beta)')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:  [ 1.01531428  1.01238373  0.97819988]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=0))\n",
    "print('  stds: ', a_norm.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.70292356126e-09\n",
      "dgamma error:  7.42041421625e-13\n",
      "dbeta error:  2.87950576558e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Batch Normalization: alternative backward (OPTIONAL, +3 points extra credit)\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For the sigmoid function, it turns out that you can derive a very simple formula for the backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can also derive a simple expression for the batch normalization backward pass if you work out derivatives on paper and simplify. After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster.\n",
    "\n",
    "NOTE: This part of the assignment is entirely optional, but we will reward 3 points of extra credit if you can complete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  0.0\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.04x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs2312n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the flag `use_batchnorm` is `True` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.26119551013\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 8.88e-08\n",
      "b2 relative error: 2.89e-07\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.17e-09\n",
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.99653322011\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 1.11e-07\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 5.69e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            use_batchnorm=True)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.106000; val_acc: 0.111000\n",
      "(Epoch 1 / 10) train acc: 0.299000; val_acc: 0.243000\n",
      "(Epoch 2 / 10) train acc: 0.433000; val_acc: 0.314000\n",
      "(Epoch 3 / 10) train acc: 0.465000; val_acc: 0.293000\n",
      "(Epoch 4 / 10) train acc: 0.563000; val_acc: 0.329000\n",
      "(Epoch 5 / 10) train acc: 0.565000; val_acc: 0.318000\n",
      "(Epoch 6 / 10) train acc: 0.613000; val_acc: 0.339000\n",
      "(Epoch 7 / 10) train acc: 0.691000; val_acc: 0.342000\n",
      "(Epoch 8 / 10) train acc: 0.725000; val_acc: 0.326000\n",
      "(Epoch 9 / 10) train acc: 0.780000; val_acc: 0.333000\n",
      "(Epoch 10 / 10) train acc: 0.757000; val_acc: 0.307000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.123000; val_acc: 0.130000\n",
      "(Epoch 1 / 10) train acc: 0.260000; val_acc: 0.215000\n",
      "(Epoch 2 / 10) train acc: 0.319000; val_acc: 0.273000\n",
      "(Epoch 3 / 10) train acc: 0.336000; val_acc: 0.280000\n",
      "(Epoch 4 / 10) train acc: 0.386000; val_acc: 0.299000\n",
      "(Epoch 5 / 10) train acc: 0.455000; val_acc: 0.325000\n",
      "(Epoch 6 / 10) train acc: 0.486000; val_acc: 0.322000\n",
      "(Epoch 7 / 10) train acc: 0.502000; val_acc: 0.295000\n",
      "(Epoch 8 / 10) train acc: 0.580000; val_acc: 0.314000\n",
      "(Epoch 9 / 10) train acc: 0.600000; val_acc: 0.310000\n",
      "(Epoch 10 / 10) train acc: 0.680000; val_acc: 0.333000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=200)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5ZedXsPYK4g8EAitYs2Zen1llCp6JiIiIiIiIiBSOAmgxtdW1BLYEErPHDHCQ\n3oG1uNiyTMeJroRNVV9touu4WPDsSroPHonMIRi0+HxL8PsX5eQ8CpGtpgw5EZHsBIPBQk9BRCQt\n/TslIiKSSAG0mOY7mllTs6ZX9hgTgdeBqb2f42x2qLu4LmFbvL6aH39XYMnrrY5lnvUWicyhpWUp\nfn//5x4KhWhsvJvW1rUZLQ/NhUIcU0RkqDvmmGMoKSnhmmuuKfRURET6VFJSwjHHHFPoaYiIiBQF\nBdBiUmWPzZk1hxfbXmSDs6HXssyKTRU0PdiUcsyu+mrhCaRrzxkOl/Q7iysUClFVNY9g8MtEIovI\ndnlofxTimCKSe8oeHXwnn3wywWCQHTt2FHoqIiJ9OuaYYzj55JMLPQ0REZGioABaN8myxyAaMOoZ\nWKurrqPpwaY+A0XGGFyuvSSuA038b5drb9Y3sfH5NTbeHQtkzel+1LwsD40rxDFFJDeUPVp4J598\nsm5IRURERESGGGOtLfQc+sUYUwm0t7e3U1lZOWjH7U/GRkPDQh544CzsmF9ASSuMC8N+F+yrxRy8\ngJtueiWjgFMoFKLxzkZaV7cSHhXGdcjFzr+8T+idV4muNe01WzyeGjo6VuU008TrraazcxWpCsPF\njykixSUxe3Q28exRx1lBRcVSZY+KiIiIiMiQtX79eqZPnw4w3Vq7PtfjKwMtS/0JQt1++4185/Gp\nHKjZB6fZw/XVNgYYs+p7fOUrG/sco6vDZ3mQSF23Gm2vAy3nwp42oOeN7x7eeWcnXm91zjJN8r0k\nVUTyR9mjIiIiIiIi/eMUegIjwV333kV4znsw1R6OOxng9AjhOe+x2L+4zzEa72yMBs/KI4ljTAVq\ngzDW1+MZIWAee/feSWfnKrZvf5bOzlUEAlVUVc0jFAr161wSl6Qm078lqSKSf62ta2OZZ71FG5qs\nHeQZiYiIiIiIDA0KoA2C1tWt0QYESUSmRGhZ3ZL2+dbatGMwNQIlPce4G/gS8DG6R9yimSY34/Mt\nyeIMEtXWzsRxViR9zHFeoK7uo/0eW0TyI5vsUREREREREUmkAFqeWWsJjwqnu2cl7ISx1ibcuIZC\nIRoWNOCt9HLSv5zEtp3b0o7BuBAQD7BZYDVwSdLdu2eapLpZTncT3dx8KxUVS3Gc5zmciWZxnOep\nqLiHpqZbUj5XRApD2aMiIiIiIiL9pxpoeWaMwXXIldh4s7v9sPuvuzl1+qldjQFmnz+bX7b9kg1T\nNxyud/ZcjfH7AAAgAElEQVRDUo9hwT3mfY72zCYcLmH06L3s2DGGvXtT3Qjv4Z3QZryV3q5j1lbX\ncvuXbueuu5b12Z3P7XbT1rYcn28JLS1LCYdLcLn2UVc3k6YmFSEXKVa1tTMJBFb0qIEWpexRERER\nERGR1NSFcxA0LGgg8Hag9xLMA8D/AS4AyjncGOBZ4J+I1jeL+zlwEnBa7/GdTQ71k+vxL/Z3Fe9P\n3SkzBEdUQd2r0bFix3S2OLieH8/BnY9g7RWk6s6XrDmAGgaIDA2Hu3DeHAuixf8/f4GKinvUhVNE\nRERERIasfHfh1BLOQdB8RzMVr1fgbHK6r3iEnwLncziQRezvXfQOlJ0LtAEbSRjD2eRQsamCJl9T\n9OmxQFbKOmVjG6Huz9HgXLdjRqZEOHDxe9gxL9KzZtqf/3wj5513FV5vNWVll+P1VtPQsLCrEYGC\nZyJDQzx7tL5+HR5PDaWlc/F4aqivX6fgmYiIiIiISBrKQBskoVAIX5OPltUthJ0wroiLnX/fSeja\nUGKSmAWeAK5OMsgB4CUYtWEUJ5x0Aq6Ii7rqOpp8Tb1ufFNlmnDUidDw15RLQbnPA//o6D4SMA/4\nInBp19aemWkiMvQoe1RERERERIaLfGegqQbaIHG73fgX+/Hj7yrQXzajjJAJJe5ogIMkr3c2FpgF\nZbvL2PLylqQ3vvEb4mR1ykaP3suOMftIWRrNAOPCPQ5+NzAfxq6Akvro4/tdRPbV8uc/34jPtwS/\nf1E/XhERKTQFz0RERERERDKjAFoBxG9aUzYXOBnYRPJ6Z5sd6i6uS7jxDYVCNN7ZSOvq1oSmAM13\nNOP3L8LvPxxY81Z62WtDqTPQ9rt6TOhFOGI51AXhtMjhOm0bA9jWNTzzzLH4/cnPU9ktIiIiIiIi\nIjIcqAZaAdVW1+JsSfIWnAu8CGajSVvvDGJLNWuqCLwVoLOuk+2XbaezrpPA2wGqaqp61SlLeUyA\njQ7sq+u2wcLYrdHg2dRIYp220yNQG2Tn3jfovgw4FArR0LAwZb00EREREREREZGhRgG0AkrVXMDZ\n5nDGiWdw4/E34mn1UPpcKZ5WD/WT62lb2ZZQc6zxzkaC5UEi5ZFeTQGC5UF8Tb7MjrnJYeyq8ZiD\n53P4AaDkzWjmWTJTIxxw3uoKzsXrrgUCVXR2rmL79mfp7FxFIFBFVdU8BdFEREREREREZEjSEs4C\ncrvdtK1sizYXaD3cXKCuuo6mBw83Bki3FLJ1dSuRuuQBrsiUCC2tLfg5vMYy3TG/8oevsHjxd2hp\nebirZtp2C++nqZk29kgXkUgEx3FobLybYPDLsaYFh3eKROYQDNohVS9Ny09FREREREREJE5dOItI\ntkEbay1lM8rYftn2lPuUPlfKtpe3pRw31THj2085+xS2zt3au2baAWBtrCNo2Qm4DrnY+Zf3Cb3z\nKjAx2ZHweGro6FiV9nwGO2jV/ZihUIjGxrtpbV1LODwBl2svtbUzaW6+FbfbraCaiIiIiIiISJFS\nF84RJNvgjDEmdSMCABttVJBu3FSPxbfPvXgugS0BIlO6ZbkdAJ4EquDQhYfYbrZH5/A60HIu7GkD\n3D1HJBwu6RWE6itolQ/Jjjl79of55S9/x4YNtxKJLCLeLeGBB57m0eVncsRxhkOjDyU0aMjX/ERE\nRERERESkuKgG2hCXrilAvGMnQH8zDZPWTFsLVBHtEtq9scBUoDYIY31JRrK4XHt7Bc8Gu2ZaqmMu\nW/Y2weDNseWn8TnuwU74Gn+f9Re2zt2askGDiIiIiIiIiAxvCqANcemaAkzdMJUDBw/grfRSNqMM\nb6WXhgUNWQV+4jXT6ifXdzU0GLVhFJSneMLUCJS09NrsOC9QV/fRhG2JNdMOR+KiNdNuxudbkvE8\n4/oKFKY6JvwFuDRx57GNsQ6kNqMGDdnOJVNDdZm1iIiIiIiIyHChANoQlyzA5Wn18LmjP4cxhu/u\n+C6ddZ0Dyp5yu934F/vpaO9g67qtnFB2QvIloxDdPi4ExJd8WhzneSoq7qGp6ZaEXVtb1xKJzE46\nTCQyh5aWtRnNLxQK0dCwEK+3mrKyy/F6q2loWJj0HJMf0wIT6HVSJa0pO5BGpkRoWd07UBgKhWhY\n0DCgoGW25yQiIiIiIiIi+aUaaMNAPMDlx99VY6xhQQMbpm4gUt4tABTPnrLR7Cn/Yn/qQVNwHKfP\numvuMe9ztGc24XAJLtc+6upm0tS0PKFmmLWWcDhJ0KrbZJPVTOspviQzmlW2iHjtskBgBWvWzKOt\nbXlCN9PkxzTAXhJPysK4cNpAYdgJ92pCUFVTRbA8GO2MGp0KgS0B1tSsoW1lW0Z107I5JxERERER\nERHJP2WgDTPxYE7r6tbEwv/dpMqeylRfddc++2/X0tGxim3bnqGjYxV+/6JewTNjDC5XPGiVTO+a\naclksww0/TFnAi90+9nAfle66fVq0NB4Z2M0eFYe6deSz/6ck4iIiIiIiIjknwJow5C1lvCozLKn\n+iNd3bWKTRU0+Zqih+nRMKDnksSJE0fhOC8kOULymmnJZLsMtLZ2Jo6zIsnetwLfwJjn6DqpfbWw\nse8GDV1zyVHQMldLW0VEREREREQkN7SEcxgyxvS5zLJn9lQ24nXXfE0+WlpbCDthXBEXddV1ND3Y\n1Gt5YaolicY8zZgxXyQctkQil3Rtd5wXYjXTlqedR3+WgTY338qaNfMIBm23DC+L4/yaqVPHM2vW\nS7zwgp9wuIRRo3az56WjeXfUzmhgLLYk09kcCxQ+2JQ4lwyDlule91wtbS0GQ2GOIiIiIiIiIplQ\nAG2Yqq2uJbAlkDQjKln2VE99BT+S1V1LJXFJYpzB2is5eNAybdp97Np1T9qaackkLslMHinsuQzU\n7XbT1rYcn28JLS1Lexzz2YR6acYYQqFQn4HCriWpOQha9uecikkoFKLxzkZaV7cSHhXGdchFbXUt\nzXc0q26biIiIiIiIDFkKoA1TzXc0s6ZmDUEb7DN7Kq6/wY++gjnRJYmLkj5m7ZXs2vUwHR2r+pWx\nVFs7k0BgRY/gXFSqZaButxu/fxF+f+pAYXxbqkBhfElqa+tawuEJuFx7mXjcsThbtvY7aDmQcyoG\nuWqiICIiIiIiIlJsTH/rYBWaMaYSaG9vb6eysrLQ0ylKXdlTq3tkT/lSLLOMBz+6B9y2OFS8XtHv\n4Ie1lrKyy9m+/dmU+5SWzmXbtmeAvoNxyc4xujz05h5LMqPLQPPRsTJxSepsui9JdX3gM4Rnv4c9\nzXa9huZ1wxmbzmDd6nVZduEcvHPKhYYFDQTeCiR2fo1xNjnUT67vV+dXERERERERkb6sX7+e6dOn\nA0y31q7P9fgKoI0QfWV35TP44fVW09m5iuRLEnfjPvZMjj5pdL+X/IVCodiSzLU9lmTekpdAU0PD\nQgKBqiQZYiHgIhh7IpT8EcaFo5083zuLMzy7efnlZ4v2nHLBW+mls64z5RJWT6uHjvaOwZ6WiIiI\niIiIjAAKoKWgAFpu5TP4kTbg5P4Q1L0B5WSV9ZYqIDgYhetTBwQXAh8BLonPpmsfx3me+vp1+P2L\nsj7eUCjGb62lbEYZ2y/bnnKf0udK2fbytqI/FxERERERERl68h1Ac3I9oDHmq8aYl40xu40xfzXG\nPG2MmZrB82YZY9qNMfuNMRuNMdfmem6SXDYdJLMdF6KdLysqluI4zxMNKhH9e9w1UPsGnMbhYxuI\nTIkQLA/ia/IljBevO+b1VlNWdjlebzUNDQsJhUKHp5rn4Ez6LplrgcRGCXGRyBxaWtb265hDIeCU\n0EQhmQF2fhUREREREREppJwH0IDzgPuBc4BqwAWsNMaMT/UEY4wHeA74GXAW4Af+2xhzcR7mJz3k\nMvgRCoVoWNCAt9JL2YwyvJVeGu9sZOXKH1Bfvw6Pp4bS0rl4PDW4j38xGjxLIjIlQsvqloRxq6rm\nEQhU0dm5iu3bn6WzcxWBQBVVVfMSgmj5lNglszsLpAqsARjC4ZKsg5BDSW11Lc6W5P+kZNNEQURE\nRERERKTY5DyAZq291Fr7f6y1QWvtK8B1wMnA9DRP+09gi7V2gbV2g7U2ADwF3Jzr+UlyuQh+xBsR\nBN4K0FnXyfbLttNZ10ng7QA182poarqFjo5VbNv2DFu2rGTisUdknPXW2Hh3rGh/vKh+dKdIZA7B\n4M34fEuyP+l+qq2dieOs6D1hkgXW4iwu194hk4HVn0Bf8x3NVLxegbPJSUg0dDbFOr/6end+FRER\nERERERkK8pGB1tORRG+n/55mn48Aq3tsWwFU5WtSkigXwY/GOxujXTzLI2mXZBpjss56a21dG+t4\n2Vv35ZGDkeGVckkqpcBPkz7HcV6gru6jeZ/bQGSyRDYdt9tN28o26ifX42n1UPpcKZ5WD/WT6zPq\n4jqcs/NERERERERkaMtrAM1Eox/3Ar+21v45za4nAH/tse2vwERjzNh8zU8OG2jwA6B1dSuRKb27\neELvJZmQedZbJBJJU3cMYA/vhDYnLBttWNCQt2WdbrebtrblvZakzp8/mYqKe3oF1hzneSoq7qGp\n6Za04xYygJSrJbJutxv/Yj8d7R1se3kbHe0d+Bf7U14/Aw3aiYiIiIiIiAyGvHbhNMY8BMwGZlpr\n30qz3wbge9baxd22XUK0LlqJtfZAkudUAu3nn38+kyZNSnjs6quv5uqrr87RWYxM2XZ+7E8XxviS\nz2B5MBp4i3fh3OwwdcNULph5ASteXEF4VJi339jJod3/HxxoBroHY0JwRBXUvXq4GUGGnTxzpftr\nFQqF8PmW0NKylnC4BJdrH3V1M2lquiXpPEKhEI2Nd9PaupZweAIu115qa2fS3Hxr3ufdXepOqQPr\nIJpOPGgXXZo7m/ib5zgrqKhYSlvb8kF9DURERERERGRoePzxx3n88ccTtu3atYtf/vKXkKcunHkL\noBljHgBqgfOstVv72PdFoN1a++Vu264D7rHWHpXiOZVAe3t7O5WVlbmbuPSbt9JLZ11n8kQxC54W\nDx3rOxI2h0IhfE0+Wla3EHbCuCIu5pw3hzVr17Dx9I1QTldQjI0OtFbAnja6gmhjG2DeAzC193Xs\nbHKon1yPf7E/tyeaob6CkMUUQPJ6q+nsXEWqN8/jqaGjY1VOj1mIoJ2IiIiIiIgMT+vXr2f69OmQ\npwBaXpZwxoJnc4EL+wqexbQBF/XYVhPbLkNEfxoRJFvyZ41l49SNhzPKiP19egRqgzDWRzSiZqHk\nf+C05EHgZMtGB1NfGXzF0hjBWtvHEtn8dBDNtK6diIiIiIiISKHlPIBmjHkQ+DTwKWCvMeb42J9x\n3fb5L2PMD7s97WHgVGPMYmPM6caYzwNXAUtzPT/Jn4E2IogHnB57+olo8KynA8D2CBxxH85JJTjH\njGf0xL9n3Mmz2BRLAMkYg8s1uB1ECxW0ExEREREREemPfGSgzQcmAr8A3uz25xPd9jkRKIv/YK3t\nBD4GVAO/B24GbrDW9uzMKUUsF40IrLXsO7S/d1zlAPAk0aumHiL/sZ/IFw7wfiSccSfPgcplMKfY\nAki1tTNxnBVJH8tHB9FCBO1ERKT46IsSERERGSpG53pAa22fQTlr7WeTbPslMD3X85HBFV+S6cef\nsgZYnw0K9scy2Lrv8hJQRWJmmgFOBTaRNGMt1bLRbOSryH9iACl53bHBDCA1N9/KmjXzCAZttyWl\nFsd5IdZBdHnOj1lbO5NAYEWKGmi5D9qJiEhxKJYGOiIiIiLZyEsNNBFIrAEWCoVoWNCAt9JL2Ywy\nvJVeGhY0EAqFej2nxBwdbRjQ3VaiDQV6OpdopbwNpF022p9vuONF/gOBKjo7V7F9+7N0dq4iEKii\nqmper7lnK9Osr8H4dt7tdtPWtpz6+nV4PDWUls7F46mhvn5d3poZNDffSkXFUhznebpq2mFxnOdj\nQbtbcn5MEREprHz/bhURERHJl7x14cw3deEcOkKhEFU1VQTLg0SmRLq6ajpbHCper+i1vHP+/NtZ\n9tgTcNk2mBqJbnwCuDrFAQ7AhEcncOwxx3Z18qyrruMrX/wKd917F62rWwmPCuM65KK2upbmO5oz\nCgjlu0vk4S6cN/fK+po69dtcMGcqK15c0a+5D1SfWYI58uabb3LplVfwpy1/xI4FcwA+eOo0fvqT\np5k8eXLejy/Dy2BdtyLSf+rALCIiIvmS7y6cCqBJ3jUsaCDwVoBIeaTXY84mh/rJ9fgX+7u2hUIh\nZsyYy2udbhj/RxgXhoNvQ/2hVKsd8bR46Fjf0XUDHQqFOKf6HIKnBaOZa7GgndlkOOP1M1i3el2f\ngSivt5rOzlWkOqjHU0NHx6psXopeQqEQPt8SWlrWEg6X4HLtY86cD/Pi+mfZMHVDRgHHoab7e5RN\nYFUkGS0FExlaBuN3q4iIiIxM+Q6gaQmn5F3r6tZogCSJyJQILatbEra53W5efvlZGj53Np5J5Uw2\nlbidyZhNyTNLutc6i2ef3Pa12wiWB6O10eJPM2BPswSnBFmwcEHaOQ9WkX+3243fv4iOjlVs2/YM\nHR2rcLn3RoNn5ZGEuUemRAiWB/E1+QZ0zEIIhUI0NCzE662mrOxyvN5qPlp9UTR4NozOUwaXloIV\nr6H65ZzkV7E10BERERHJhgJoklfWWsKjwuk+KxN2wr0+LHcPLP3lL8+yffOr/NPmf8LZ5KStdRb3\n2NNPJG0sAMBUePQnT6SddyG6RMbHyjbgWKzi72mqIMcfX986LM5TCqex8W6CwS93WwINYIhE5hAM\n3ozPt6SQ0xtxkgXKGxoWKpApXdSBWURERIYyBdAkr4wxuA650n1WxnXIlfbDsjEmWuR+ZRv1k+vx\ntHoofa4UT6uH+sn1vZb6WWvZd2h/2qDdvkPv9fkNd6ZF/nOpvwHHYpE00+yjV/Wo8xYzbvSQPU8p\nDq2ta4lEZid9LBKZQ0vL2kGe0cilbEDJVCF+t4qIiIjkwuhCT0CGv9rqWgJbAkmzjbovv+yL2+3G\nv9iPH3/fxcL3xzLVUtRMY3/fsePm5ltZs2YewaDtVeQ/2iVyeUbzzkZCwDHF3PsKOBbK4aYIXyYS\nWURXUTM+CvQsFm1g/9A8T0lUqML92SwF03WUf4nZgHHxbECLz7dEheEFKMzvVhEREZFcUAaa5F3z\nHc1UvF6R8fLLTPSVsVZijoaNKS7vjQ4l5ug+b6rdbjdtbcupr1+Hx1NDaelcPJ4a6uvX0da2PG8F\nymura3G2JJ97NgHHwZZ8OR3AMSQNcuyrTfkeFfN5FrPBytgrhqV6WgpWXJQNKJkq1O9WERERkYFS\nF04ZFKFQCF+Tj5bVLYSdMK6Ii7rqOpp8TXn5sDx//u0se+wJuGwbTD3c4ZGNDjxXxvxPX81DD30z\nqzEHK5MlZXfKzdGAY7F2p0zdWa0aSLY9BEdUQe2rMJUhc57FZrC7UCZmGs7mcPbICioqlnbdAA/G\n/y8NDQsJBKp6ZD1FOc7z1NevU9bTILDWUlZ2Odu3P5tyn9LSuWzb9owCmtKLskRFREQkV/LdhVMB\nNBl0g/FhORQKMWPGXF7rdMP4P8K4cHTJ4HvTOMMT4uWXny3q4MxgBxwHKv0N9EKgit7LOMGY5Uyb\n8S12hf82JM6z2GQazMqldEErY5Yzbdp32LXr0CAH825OuhRM2SyDJ3UAHcDi8VxMR8fqwZ6WiIiI\niIwg+Q6gqQaaDLrB+KbZ7Xbz8svP4vMtoaVlHwcPjmfMpPeo+8zZNDXdUvQ31VnVe8tAvoOWicvp\neh7nVmAe8D7wMRKDHMv41arVg5axNNwUou5UdKlesjFDWLuMP/zhi8ClxN/nQGAFa9bMy0swK74U\nLPr/+VLC4RJcrn3U1c2kqUnBs8FUWzuTQGBFimxAFYYXERERkaFPGWgyIgy34Ewm5zPYS/v6zkz6\nbiwzqXuQo/iDmcWs76yfGjo6VmU1Zrprq7+Zht2XU+bz/8Xh9v/5UKJsQBEREREpNGWgieTAcLip\nziYglqojZj6zgdJ3VlvGr341eLWxRoJcdqHM9NpKn2m4FliUdPxIZCbf//5XaGn5dV6DubquCkfZ\ngCIiIiIy3CkDTWQIyLbWVaGKq4dCodgN9Fplmg2CXNSdys21ZYHLgWSZaSGiS3gTl3bms06bFJ4C\n5SIiIiIy2PKdgebkekARyb3EWlfxm9J4raub8fmWJOwfrVM1O+lYkcgcWlrWAtGb3Fxyu934/Yvo\n6FjFtm3P0NGxCr9/kQIkeVJbOxPHWZH0sUzrTmV7bTU330pFxVIc53migbO4HT1+jrsbuJnD9e/S\njy/Dg4JnIiIiIjLcKIAmkkQ2gaXByOLMNCAWn0/6pX17eOednXi91ZSVXY7XW01Dw0JCoVBO5zwU\nbqCHagZuXPJglsVxnqei4h6amm7pc4xsri04vFSvvn4dHk8NpaVz8XhqOOusI3CcF5KMspZkddFS\njS8iIiIiIlKMFEATiQmFQjQsaMBb6aVsRhneSi8NCxqSBpay2Xegsql1BT3rVPWaOTCPvXvvpLNz\nFdu3P0tn5yoCgSqqqublZf7FJhQK0dCwMO8BxFRyGbRLFcyqr1+X0dLIbK+t7sftmWn4q189RUXF\nPT2CeRFgVNbjS+HovRARERERSU5NBESI1YGqqSJYHiRSF4mXaSKwJcCamjW0rWzrCkZks28y2dYG\nSl+4HcDicu1NGLO2diaBwIokNdDuBr5EtBZV1xFiy+ksPt+SvNRGKxaFaK4QP26+OqLGg1l+/+Bc\nW8nGiM8jWRH5nTv/QSjU//El/wa7Y6+IiIiIyFCkDDQRoPHOxmhArDzSvUwTkSkRguVBfE2+fu0b\nN9Csp2xrXaVa2gergUuSjjMSltNlW+8rF+JBu0CgKu9Zf/0JROWijlpcssy06677WM7GzzVlWw3u\n9SkiIiIiMpQpgCYCtK5uJTIlkvSxyJQILatb+rUv5OYGNdtaV8mW9p1yysVMmDCGkbycLtt6X7lQ\niKBdNnJRRy2ZeDAvH+MP5Bot9BLeYlPs16eIiIiISLFQAE1GPGst4VHhdHElwk4Ya21W+8bl4gY1\n01pX3Y/bPRto69an6exczbHHjiJ5bTQY7svp+lvva6AKEbTLxkDrqA3W+LkIfCnbqrdivz5FRERE\nRIqFaqDJiBavGeU65EpXBgrXIVdXYCmbfSF+g7oo6fGjN6hL8fv7nmuqWlfxwELP+kW3334jd917\nF62rWwmPCuM65GLiccdi3ngaa6/sNX6hl9PlWy7qfWUrm6BdIQOXA6mjNhjj56p2XWIwO27k1ADs\naahcnyIiIiIixUAZaDLiJOugOXH8RJwtyf93cDY71F1c1/VzbXVtxvvmK+upe/AsWUbNAw+cxaln\nTSXwVoDOuk62X7adzrpO/nR6O2OO/neMWU4xLKcbbLms95WJ9B1RoRiz/vI9l/6Mn6tlhsq2SjQU\nr08ZuobS7woRERGRZBRAkxEl3kGzZ2DplZNfwbXChbPJSai572xyqNhUQZOvqWuM5juaqXi9IqN9\n832DmiqwYMf8ggM1+5I2OgjPeY9pM76Vs+V0J500d9DrSPX3Rixf9b7SGeygXTJD/cY1F4GvQi3h\nHUz9mXsxXJ8yfKnmoIiIiAwnWsIpI0pCB804A/YMy0F7kGkd09j1512EnTCuiIu66jqaHmxKCCy5\n3W7aVrbha/LR0tqSdl+I3qAGAit6LBuLGugNasrloSWtcFrym+nIlAi7/vw3OjrW9Xs53YwZc3mt\n0w3jN8O4MOxycf93J7Bq1VxefvnZAdfNSnXcxsa7ey1VbW6+NePjxetx+XxLaGlZSjhcgsu1j7q6\nmTQ1DbzeVzLNzbeyZs08gkHbLdBpcZwXYkG75Tk/JuTm9SoGuVpmWIglvINhoO9zoa5PGf5ytfRa\nREREpFiYofptuzGmEmhvb2+nsrKy0NORIcJb6aWzrjNl/TJPq4eO9o6sAkt97Xv4JuLmpDeo/b2J\nsNZSVnY527c/2/tETiyDG7enfG7pc6Vse3kbkP2Suvnzb2fZY09A7TY4LRI/HdjowHNlzP/01Tz0\n0DezO5k+JN6Izebwa7iCioqlXa9htgHBwartFAqFYkG7tT2CdrfkLdiYyes1VHi91XR2riLV/7ge\nz8V0dKzuc5yGhoUEAlUpgtnPU1+/bkjVQMvV+zzY16eMDMPt/zcREREpfuvXr2f69OkA062163M9\nvgJoMmJYaymbUcb2y/oOLOU6qJKvG9SUgYWjvNDQmTzesB/cj7o5+piju5oL1FbX0nxHc0ZzmXhc\nGaGL34Spkd4PbnBwry5l99+29ud0Ukp3I2bMcqZN+w67dh0aEplWgxG0G243rrk6n4EEs4uxkH4+\n3udiPE8ZmvoOfNfQ0bFqsKclIiIiw1i+A2iqgSYjRkK3zWSSdNDMlXgXwo6OVWzd+jQdHavw+xcl\n3KzntH7RvtpoRlhPB4AfQej8UEINuMDbAapqqvqsS2OtZZ/dGc08S2ZqhH3syHkdqdQ1sEJYu4w/\n/KEhoYlCIFBFVdW8oqyzk+r6yuVrNtyK5eeqdl18CW99/bqMagAWe/2mfLzPCp5JLoyEmoMiIiIy\n8iiAJiNKNh00c6l758+TzzkZb6WXhgUNvPnmm706gjYsaMgokAWpAwvm4AWMXTW+V6MDfgqcD5xG\nr+YCwfIgviZf3yczLpLungjGpgiu9VP6G7G7gS8DH6P7CWXbnTHZMQdDPgI0+bpxLeSNbraBr77G\nigezt217JmkwG1J3uC2G4Ky1VgGKQabXMTvq8CoiIiLDkZoIyIjSfEcza2rWELRBIlMO1+9yNsc6\naD7Y1OcY2Yp3/gyWB4nUHT7mAxse4Dtnf4dwTThhe2BLgDU1a2hb2ZZwUx8KhWi8s5HW1a0JSy9X\nrvwBixd/p1dR/K98ZSOL/YsTGh3s3LWT0GnJb/wjUyK0tLbgx5/yXIwxlIwaR8geSFlHrmTUuLQ3\nRS+kAtgAACAASURBVNkuEUtf/H0tsCjp86IZOEvxpz6dBINddD9fBbZzWSy/mBoRxANffn/ulhmm\nGyOxw23XM2LBWYvPt2RQl8Emey92736X4dYUoZgU0/U/FOWzgY6IiIhIISgDTUaUeAfN+sn1eFo9\nlD5XiqfVQ/3k+l4Bq1xJ6PzZLevLvmU5cPGBXtuTZYPFg3CBtwK9ll7WzKuhqemWXhk1kydPxr/Y\nT0d7B9te3saW321h4jET02aPhZ1wn5kWn7rik/B6igc3wqevvLrX5u4ZeNlk2sUlX6pqgdxk4BQi\n2ygxQJO77DlIs7SXzG9cizkDazCCQsW0DDbVexEKfZBoWmlvClAMTDFf/0NFrpZei4iIiBQLBdBk\nxHG73QmBpY72DvyL/XnLKGhd3RrNdutpK1Ce/DmRKRFaVrd0/ZwqCNcz2JYqsGCMyaoGXLqg07e/\n8W0qNlVgNpqE5aFmo6FicwXf+vq3EvZPF/zLpO4apLoRA9hBLpYI5TOYlUo+AzS5uHEtxGvSU6GW\nzRXb8shU7wXcDzRhzHMoQJFbxXD9D3W5XHotIiIiUgwUQJMRLd+ZLNZawqPCve/DLTCGjLPBUgbh\n6B1sSyddDTjzmmHS+El9Zom53W7WrV7HTSfdlJDFd9NJN7Fu9bpeN0WZBv/SSXUjdtZZR+A4LyR9\nTjYZOIOdbZTvAE0ublwLlYFVDIX7i61+U+r3wg2s4Igj/n8FKHKsmDIQh7JMaw6KiIiIDAWqgSaS\nRwlZX93vtQ1wkHTlixKywZIG4bqNFQ+29XVDn6oGnHnNMObnY3il5pWE7anqscWz+Pz4+zxu6+rW\naI23JDKpu5ZwzB41sA7XEaNbpojFcV6IZeAs73PcbIJZuQqY5LJOWSoDqRlWiNcE8lcXrj+KpX5T\n3+/FRCZO9LJlyzOAumjmQqGu/+FOr5WIiIgMdcpAE8mzlFlfJwObkj+ne0fQbJZe9iVVDbhp26ZF\nmxn0I0usr4YBmQb/shE/Zi4yrQqVbZSLOmWZynbuhXpNimnZXLHUb8rmvVCAIjeKLQNRRERERIqD\nAmgiedZ8RzMVr1fgbHISa4adYBi7amyv7c6mWEdQ3+GOoOmWXnYPtmUSiEpWA27Xe7tyskS0p1wG\n/1LJZIlQX6/LYAaz4oolQJNKIV6TYlo2V0z1mwrxXox0es1FREREpCcF0ETyLFXW102n3MSW/7sl\no46gqYJwziaHqRumcuDggX51uMx2iWh/ZBr8y4XugbhsamllEszKdcH4YgrQJDPYAb5iK9wPxVO/\nqdiDrcORXnMRERER6ckUqsvZQBljKoH29vZ2KisrCz0dkYylqpvTVz2dUCiEr8lHy+oWwk4YV8TF\nnPPm8GLbi2yYuiGhdpmzxaHi9YpegbhUvJVeOus6U9Zj87R46FjfkflJ9ph3VU1VtJFA9zlujmba\nZTrHrI/ZVUtrNodro62gomJp0gBVKBTC51tCS8tawuESXK59zJnzYayFFSt+Szg8AZdrL7W1M2lu\nvjXncy7GekrJXpO6upk0Nd3Sdf65nLfXW01n5ypSXYgez8V0dKzOybGGmkzeC8ktveYiIiIiQ8v6\n9euZPn06wHRr7fpcj68AmsgQFA9aNCxoIPBWIFq7rAdnk0P95Hr8i/su0N+woIHA24GkyzizGSeV\nZMG/uuo6mnxNebkRbWhYSCBQlaIA/PPU16/D71+U8vnWWvbs2ZN1EG446x4oC4VCNN7ZSOvqVsKj\nwrgOuaitrqX5juYBvSYDfd9GimIMtg53es1FREREip8CaCkogCaSQeZYq4eO9r4zxwYzS2wwbkT7\nzmSqoaNjVdoxFMxJLuW1kmXWY8qxq+YRDN78/9i7/yA3zvNO8N9uCpRMCpRlhbbM8UgASVGF1FnZ\nDB2uJ1Rs1+1wRGcFUDGvKqXabJzk7uRcDoFNiaGTmqHJWw4qYSJRRsqw11FdNqnsltdbJZkEVOGP\nmaIjOzSXimcuCSNP+EMEpMmYVCTZIpsji4QHfX9gwBlguoHuxvt2vw18P1UqSUNMo3+83cT74Hmf\nx7Kraq8FLYmIiIiIyDnZATTWQCMKKZG1y+zqtFnVY+uU7OCZqFpaKhW0V8nIgZFa8MxDx9Z2VK8L\nR91P9peKQXxpGdYvSomIiIhUc1vQO0BE3jR0uLTJQHPT4bLenTOHXKiXK2mahkhkDq1OTCQy1/L4\n3AThZJ4nFa9DcaKIaqpFx9ZiATl4X+5bL9yfy6l5/NR9DMPAyMjTKBZPSal1KHv7qrwnERERUbdj\nBhpRiMnqcBn2oEUyuRW6ftzyz3T9GFKph1v+fmMQzkr7IJxXbrqH+k12x9Zlmwv5OCT11ZcN5/OD\nKJfHMTt7BOXyOPL5QQwO7uz4vpO9fVXek4iIiKgXMIBGFGLZvVkkLiSgX9QXYz1mrfB/4mICY6Nj\nge5fULLZ3UgkDkHXj2LpidH1o0gknsXY2FNtt9FpEM4L1Se+DVmPVlxmPRIFbWTk6YVGIfWaewCg\noVrdjunpXRgdfUbp7avynkRERES9gAE0ohDzs3ZZmDTX0lq3LuW6lpaIIJxbnU58/ah1JCvrsVew\nHpVaZNc6DKKWIus3EhEREcnBABpRyNVrl5UmS5h5eQalyRJyB3M9Gzxbyrz9R8DdF6GtmwLuvlj7\nf4eCKGjvZeJrGAYyezKID8TRv6Uf8YE4Mnsy0rLVmPXonsrLcnuZqIYjQW1flfckIiIi6hVsIkDU\nRbh0rsYwDAwOD9a6RaYWukWaQP5SHieHTzrOznNS0F5UoXsvjQtEHacb9azH0bFRFIoFVPQKItUI\nUkMpjH11jIHbJvVlubXMwv2oX6R8/jhOntzJ7qIBEtFwJMjtq/KeRERERL2CGWhE1HVGDozUgkob\nq0tXQqK6oYrpjdMYHRt1vc2lE04ZWV9uGhfUs0dkHKcTzHps79Y16vJ6VG4zmVTLfJJd6zCIWopB\nvCcRERFRL9BU+zDrlKZpAwAmJycnMTAwEPTuEJFC4gNxlFNluwQMxIoxlCZLnrbdkPW1YTHrS7+k\nI3Eh0VHWVyazD/n84EKwpZGmPY+HHvozXL06j0plNSKRObxtfh/Gb1yVcpzLNico066bGYaBkQMj\nKE4UUVlRQWQ+grf/5acw3nwFwBqL3zARiw2jVBr3e1c7YnWcyaEksnuzlmPfMAyMjDyNYvHUrbGb\nTG5FNrs78KDrYobgriVBThO6fgyJxLMdZwjK3r4q70lERESkgqmpKWzevBkANpumOSV6+1zCSURd\nxTRNVFZUWq2EREWveA4INWR9LdlmdUMV02Yt6yt3MOdp37PZ3Th5ciemp82Gia+mvYCVK/8AZ8/m\nlvy8Cnz4g9KOE3AfKOlldstpcQFA4ReB66cBNJ+z5ctyl1IxaOl22bDqS1jrtQ5HR59BoXAIlcoq\nRCLvIpXairGxzvdN9vZVeU8iIiKiXsAMNCJSltcAQtsMtEIMpSlvmVkys9uAWsChNvE9dWvie9dd\nOs6e/QKq1U83vvjuOJBpsS8dHKfMTLtulNmTQf5yvjGwWndOB15IAzeaA6smYrFtKJUmbv1E5Wwt\noPVx6hd1pNelGwLIrbIqdf0o0ukzyOX2y9xlV2QHLYMIiqoYiCUiIiKSQXYGGmugEZFSRNQXSw4l\noV+yfrzpr+pIbUt52jc32W1e1RsXlErjmJk5jFJpHFevzlsGIPBuEjgv/jiB4OqrBc3rtStOFGuB\nRiubqsCqwrIfN9ejqmdr5fODKJfHMTt7BOXyOPL5QQwO7lSia2er46xuqKIw0XicXjrLBkl2oCmI\nQBaDZ0RERERiMIBGRI74ka1az3rKX86jnCpj9tFZlFNl5K/kMTg86DiAkN2bReJCAvpFfbEmv1nL\nkElcTGBsdMzT/mmahsh8pFWdf0TmI8ImrPWGAbbdOW9kgWICOAehxwm4D5SEmWEYyGT2IR4fQn//\nY4jHh5DJ7HM83pwEVnGHAaB+Pk3o+lEkEs9ibOypWy9TveGA2wCym86yRERERESqYwCNiGx1Glhw\nS1TWUzQaxekTp5Fel0asGEPfi32IFWNIr0t3vPRQZnabldbdOaPA9e8hOvERocfpR6adKkRkfTkJ\nrEZX/hSx2CPo69uBWGwY6fSZZfW/VM/WchtAdtNZ1o1uGHdEREREFD4MoBGRpSCWk4nMeopGo8gd\nzKE0WcLMyzMoTZaQO5jruI5Udm8WD557ENp5rSHrSzuv4cHzD7rK+nK6XDWZ3ApdP265DV0/hd98\n/H8Xepx+Z9oFSVTWV7vA6m/+6mcbluXmcvsbrlFYsrXcBpBbj93GJayt+B3MJyIiIiJqxgAaEVny\nezmZm6wnt0EE0YEe89q9MF9IAX8aA77eB/xpDOYLKZjX7nW8DTfLVbPZ3UgkDkHXj2Jp1K55GaDI\n45SVaacaUVlfTpcN210jWdlaorldHu107LYShtpwRERERNT9GEAjIkt+Lydrm/X0HnDtjWtYv3m9\n5+YCIoyMPI3z5/cA7x0GflwCLs/U/v3eYZw//3uOA4tulqtGo1GcPv080ukziMWGWy4DFEVWHTmV\niMz6ErFsWFS2lkxuj1PE2FW9Nlyngs4qJCIiIiJntLB+cNM0bQDA5OTkJAYGBoLeHaKuYpom+vsf\nw+zsEdvX9PXtwMzMYaEZMZk9GeSv5Jcv47wB4K8AfBLARtTm0CagX9KRuJDouK6ZG/H4EMrlcVgH\nXUzEYsMolcbbb2cgjnKqbLcZxIoxlCZLlr9rmqYvmUiGYWB0bBSFiQIqegWRagSpoRTGRsd8O9+y\ntb+e21AqTbjerpdrVM+0mp7etSRYZELXjyGReFZasLQTbo/Ty3kRdc+pxDAMjIw8jWLxFCqV1YhE\n5pBMbkU2u1u5a0xEREQUFlNTU9i8eTMAbDZNc0r09pmBRkTLWC8na/xvGcvJ7LKe8NcAPgHgAXTU\nXKBTojKWOi3S79cyPll15FQiK+vLyzUKItOwU26P0+r1re6XsNSGc4NLUomIiIjCiQE0IrKUTG6F\npn0LuD0D3B0HPtxf+/ftGWjaC1KWk9ktD4tejdaCZxaWNheQPYkWVacqjEX6VdoXt1qNCxE1ukSK\nRqPI5fa3bDjQDZw2BQhLbTg3un1JKhEREVG3YgCNiCz9/u9/Divv+XVg51eATBn43Gzt35/JY+U9\nn8UXv/iElPdtznq69P1LWPMza+wTUG4Cb771ZttOlqKIyljqlSL9QXEaoFE56ytMQSE33GZghaE2\nnBuy6kuGKQuPiIiIKIxYA42ILGX2ZJC/nK8VuW+iX9SRXpdG7mDOl32xrRd2A8A3AXwci8s7JddG\nE1Wnqt6Fc3rjdK3mW33fX60V6fezrlu3WbxGTy4EKurX6DgSiUMtr5Ff9eV6WSazD/n84ML900jX\njyKdPoNcbv+tn4WxNpwd0fUlWUuNiIiIaBFroBFRIIoTxeXF/BcsXTbpB9tsre8BGASwCb7VRhOV\nsSSiayNZ62SJXDcGz1T7osxtBpbKWYJuiVySylpqRERERP5iBhoRLWOaJvq39GP20Vnb1/S92IeZ\nl2d86wZpla2FPwfwW/DUybIVN1lIojKWmPkkTjd2bXRL1cwkERlYYb9X3Gbgyd4OERERUbdgBhoR\n+U61IvdW2Vr3F+7H6vet9tzJsplhGMjsybiupSbqHIQ5IKAK0zS7smujWypnJonIwAr7vSKqcYWs\nWmoidPP9RURERL2LATQisqRakfvm5gLlqTLWrl4rJMhXz3DLX86jnCpj9tFZlFNl5K/kMTg8yKVQ\nPvA64W4OfK7fvB7Xbk4BuGb3TqHr2uiW6l0eu60pgFsilqSqGCh22riDiIiIKKwYQCMiS9m9WSQu\nJKBf1JcmSUC/WCtyPzY6Fti+1YMfooJ8IwdGastDN1Z9q6VGnU+47QKf14dngehDAJZvpxcCNCpn\nJgHiMrDCLBqNIpfbj1JpHDMzh1EqjSOX2+94ea3IWmoiqJz1SL2LmZBERCQaA2hEZCkMRe5FBflU\napjghMxJgV8TDhETbrvAp/mACTz6GnDHr6HXAjQqZiY187spgOqTaK9BLpUy+VTPeqTewUxIIiKS\niU0EiMiRIAp3O3lPwzAwOjaKwkQBFb2CSDWC1FAKY6NjjpdCqdQwwY5hGBg5MILiRBGVFRVE5iNI\nDiWR3ZvtOOAgc9t2RBRAjw/EUU6VbZtIRP/i/bhH+xgqlVWIRN5FKrUVY2NPKRH8lal9E4VtKJUm\n/N4tWzKeLX6N6SAbGtSD0NPTu5YErkzo+jEkEs/62qFU5cYdYW86Qc4t3hNPLmTh1u+J40gkDoWu\nay8REbknu4nAbaI3SETdyc+lQG66B9Zro+WQ8zRRamiYYBOI8bNhgpWGLqSpxS6k+Ut5nBw+2VFG\noMxtt1JbZrjf8s9qywwPIZez/33TNFFZUWnZRGLN2tW49PKJ2v9Kun4qTs6Tya3I54/bBCcXM5NU\n2XcZwTOZY1qVDqf1TL7R0WdQKBxqChSLDRS0Gitush5V/XuEukNjJmRdPRPSxOjoM+xMS0REHeES\nTiJSRqfL+jwvhZLQMEFkdq/MGm1B1H8TsczQTadYGQEalZcItaoxtmnTn+DGjRvK7rsIMse0arW+\nOq2l1orTcc56bKQK1es/EhFR+DGARkTK8LuOTj1AI6qWmqzAiswabUHUfxM14Q6iU2wYJud2Ncae\neOI70DQdzz33SWX3XQSZY1rlWl8iA1RuxznrsVHQwlD/kYiIwo8BNCJShh/fHhuGgcyeDOIDcfRv\n6Ud8II6RAyM48fyJjhomyAqsOFmqWNErniYFMrfdjogJdxCdYsMyObfKTIpEVuLcud3K73snZI/p\nXslwcTvOVeqs2ivXiBqplglJRETdiQE0IlKCH98e12sj5S/nUU6VMfvoLMqpMvJX8hjeOYyx0TGU\nJkuYeXkGpckScgdzjpdCyQqsuFmqqNK22xEx4Q6iU6zTyblKWQ7169cLgQWZY7qXMlzcjhW/O6va\n6aVrRMuplAlJRETdiQE0IlKCH98eO62N5OU9ZAYnZC5VDGIZJCBuwl1vIuE18OlG+8n5dbz55ttK\n1hfrpcCCrDHdKxkuXseKzHpsTvXKNSJrKmVCEhFRd2IAjYiUIfvbY1m1kWQHJzpdqtjqfYNYBlkn\nesIte1LcenJuANiJubkDStYX66XAgswx3QsZLiLGSpDjqBeuEVlTJROSiIi6FwNoRKQMmd8ey6yN\nJDs44WWpolWtt8yezLJAThDLIK2EJXBjPzl/GsAXAPx7qFpfrFcCCzLHdK9kuIR5rPTKNSJrKmRC\nEhFR99LCulxD07QBAJOTk5MYGBgIeneISBDDMDA6+gwKhVOoVFYhEnkXqdRWjI091fEH4PhAHOVU\n2TqIZgKxQgylqZKnbWcy+5DPDy7UQGuk60eRTp9BLrff07abmabZMuBUr/U2vXG6lnGnoZaBc0lH\n4kKiZRCh3bY7JXv7st+z3ixienrXknp3JoCHAfwt7AZXLDaMUmlcyD54Zbfvun4MicSzLTM0grhu\noojed5nPKFV0MlZU0AvXiIiIiJabmprC5s2bAWCzaZpTorfPABoRKUv0xDezJ4P8lbzlMk79oo70\nujRyB3Oeti1jwun1+DN7MshfztdqvTXp9Di9MAwDIwdGUJwoorKigsh8BMmhJLJ7s9Ims17f08k5\nXzo5v3nzfYhE3sVbb81jbu7btr/T17cDMzOHAw9CuQksGIaBkZGnUSyeQqWyGpHIHJLJrchmdzMI\nsSDMgcV2uiUI1c3XiIiIiBoxgGaDATQicss2M+vVWm2kTpd3iZhwigg2tc20K8ZQmvSWaedWJ9lw\nfr2n23Nu9fq3/+WnMN58BcAaiz0yEYttQ6k0IfQ4O9UqsLAYEH5yoTlGPSB8HInEIeUzkEgsBqGI\niIgoDBhAs8EAGhF5YRgGRsdGUZgooKJXEKlGkBpKYWx0TGhAwMuEU0SwyTRN9G/px+yjs7av6Xux\nDzMvz/gyIQ4iG87Ne3oJtlm9XruowSzcBxhnATReI9FLeP3g55JkIhHCvjycuhvHChGRP2QH0NhE\ngIh6SjQaRe5gDqXJEmZenkFpsoTcwZzwbBovH5RHDozUAjMbq0vr0KO6oYrpjdMYHRt19L6R+Uir\nfgaIzEd8+yAvq/OpqPd0e87tXm8+YAKPvgbc8WvohsLlxeKphcyz5arV7SgUTvm8R0TLGYaBTGYf\n4vEh9Pc/hnh8CJnMPqldb4N4TwonjhUiou7DABoR9awgvg1ulfUrKtiUHEpCv2T9eNdf1ZHalnK0\nnU7J7Hwq6j3dnvNWr8cmIPqh7yAWG0Zf3w7EYsNIp88IX+4oO3PcNE1UKqvR6iRWKquk7wdRK/Vl\nxvn8IMrlcczOHkG5PI58fhCDgzulBCmCeE8KJ44VIqLuxAAaEZFkhmEgsyeD+EAc/Vv6ER+II7Mn\n0/ABWmSwKbs3i8SFBPSL+tJkKOgXa7XexkbHOj8oB4LIhnPznm7PuZPXr1m7GpcuncDMzGGUSuPI\n5fYLCZ45GUOiaJqGSGQOrU5iJDLH5UgUqJGRpxdq9NWbtgCAhmp1O6and2F09JmueE8KnpcvCzhW\niIi6k/AAmqZpv6RpWkHTtFlN06qaprVMddA07ZMLr1v6z7ymaR8UvW9ERH6r18zKX86jnCpj9tFZ\nlFNl5K/kMTg8eCsAIjLYFI1GcfrEaaTXpRErxtD3Yh9ixRjS69JSivZb7u7ChCOIbDin7+n2nLt5\nvcjgktMxJFIyuRW6ftzyz3T9GFKph4W/Zzdhdp58QSwz5tLm3tHp8kuOFSKi7iQjA201gL8H8Duw\nn2Y0MwE8AODehX8+bJrmv0rYNyIiX7mpsSUy2ORXrbelrLKkbt68iQfPPehrNpybDDy35zyIgKCI\n2nhuZbO7kUgcgq4fRTfUdPMD6x35J4hlxlza3Ds6XX7JsUJE1L2EB9BM0zxmmuaXTNM8Avu/Oay8\naZrmv9b/Eb1fRERBcFNjS9bSSz+W2tllST33o+dgmiaeWPuEb9lwbjLw3J7zIJbHBtGIIRqN4vTp\n55FOn5Fe060bsN6Rv4JYZsylzb2j0+WXHCtERN1LlRpoGoC/1zTth5qmndA07ReD3iEiok6Ypum6\nxpYKSy+9apUldf7B81gZWelrNpzTDDy351zGNWqVhRBEI4a6aDSKXG4/SqVx4TXdug3rHfkviGXG\nXNrsXZiyrUQsv+RYISLqTprMv9A0TasCeMw0TduvxzVN2wTgkwC+D+B2AP8ngP8IYItpmn/f4vcG\nAExOTk5iYGBA7I4TEXlgGAZGDoygOFFEZUUFkfkI3v7x2zA+a1gHQEwgVoihNFWy3J5pmqH5hjo+\nEEc5VbY/zmIMpUnr41SJ23Pu9RoZhoGRkadRLJ5CpbIakcgcksmtyGZ3LwtQtT23LcYQ+SMeH0K5\nPA67ixSLDaNUGvd7t0Kt3b1Vz/qbnt61JHBpQtePIZF4VkqmZBDvGWZunnOqME0T/f2PYXb2iO1r\n+vp2YGbmsHLjk4iIgKmpKWzevBkANpumOSV6+7eJ3qBbpmmeB3B+yY/+p6ZpGwDsAvDZdr+/a9cu\n3HXXXQ0/e/zxx/H4448L3U8iolbqSxinN06jmqrWPysDRwBcALBp+e+0q5kVluCZmywp1Y/Jbv/s\n9t1r8Kw2sXoS1ep+1AdLPn8cJ0/uXDaxSg4lkb+Ut1zG6aTuWhjOe5i5qXfE69Cam4BLfZnx6Ogz\nKBQOoVJZhUjkXaRSWzE2Jic4EcR7hpXb55wqGpdfWgfEnSy/5FghIpLvG9/4Br7xjW80/Ozq1atS\n3zPwDDSb3/tjAFtN09za4jXMQCMi37Sb/Gb2ZJC/nK8tYVzqBoC/ArRPaDAfMG8F1vRXazWzVF+a\n6VQ3ZklZZRQmh5LI7s12dM0ymX3I5wcXshIa6fpRpNNnkMvtb9iPW8HZDVVHY0jWvpO19hlo21Aq\nTfi9W6HSGHB5BIsZO8eRSBxqG3AJIkDJoKg9t885lcjYd44VIiJ/yM5AU6UGWrN/A+By0DtBRL3N\nqqtkZk/GsiC4baH32wH8GnDnd+8MXV0zN4LoTimTXVOE/JU8BocHOyoKv7y+zuIXWVb1ddzWXZO5\n72SN9Y46J6Jwu98YELEnoo5YUGR0IeZYISLqDsIz0DRNWw1gI2qffqYAPAng2wB+ZJrmjKZpfwhg\nnWman114/ecBlAC8AuAO1Gqg/d8Atpmm+Tct3ocZaEQkjW3WzyUdiQuNWT+maaJ/Sz9mH5213V7f\ni32YeXkGQHd+kPaSJaUy24xC1DpuptelkTuYc73dxfo6/xW4fQRYVQTuqADvRYB3k8CNLPr6fg0z\nM4cBWI8Vz9mQHe67GyKyLcKUscF6R51jHbnuIaqOmNv3FPm8MAxjYfnlqabll0/xXiYiUlgYM9A+\nBuD/AzCJ2tc2z6AWSPt/Fv78XgD9S16/cuE1/wjgbwB8FMC/axU8IyKSrVVXyemN0xgdG731Wk3T\nEJmPtOpYj8h8BJqmhSYg4FaYO4hasc0oRG0MFCZcVSa4RdM0rFhxFbhzENiZBzJl4HOztX9/Jg+s\n3oKrN76P9ZvX22Y9thtDsva9HTcZmy23kdmHeHwI/f2PIR4fQiazT/msuXq9o3T6DGKxYfT17UAs\nNox0+oySwTPVOiK6qSNH6rC7Ho11xCx/01EdsXZkPi/YhZiIiKwIbyJgmuZLaBGYM03zN5v+/08A\n/Ino/SAi6kRxolhrBmChuqGKQrGAHBazeDot9N4NotEocgdzyCEXquyhZrKbIrz/3nfx+sM/ADYt\nmVxqAGJV4O5/xvVPANcfwK0svvylPE4On3QUiAyqoYNdEw03++5n0XEZ47M+4c7l7Lcf5H3hpEB/\nUPsnqnA7yee00UMyuRX5/HGbOmKdL2v283nBcUdERHWq1kAjIgqMmyBEXXZvFokLCegX9aUlhTlO\nuwAAIABJREFUU6BfrC1hHBsdk77fKhEx4Qgq28RNRqEX79x8A3jAYuPfA/BJ1Dq2tsl6DGrf7bjJ\n2LTdRoc1sNrxM7tt6fkVkZnXqXqwIZ8fRLk8jtnZIyiXx5HPD2LLlh347d/+g8Cz/lhHTn2txtHg\n4M6GMSOjjthSsp8XqmIWJhFRsBhAIyJq4iUI0W1LGIOiQrABkNcUwTRNzN82bx2cfR21CqIW3Cy9\nDKKhg4hlozKLjruZ+IukSkMH+2DDVvzzP/8EX//6L/l6XqzIDrhQ59wErWQvaw5zkwK3wrq0nYio\nGwlvIuAXNhEgIpkyezLIX7FZkumgEHuYlzAGxU3jhsD2RUBThPhAHOVUuTGIZgL47wAet/+9eiOK\nduPK74YObppo2O277KLjmcw+5PODNsvJjiKdPoNcbr/r7bZ9XwUaOgCtCvTvA/BxAJ9evn8Sz4sd\nFm5XWyeNHkT+nRhEk4KgNC5VfQSLDUqOI5E4pGSNRSKiIIWxiQARUeh1uiQz7B/agyBiGaAbrb5A\nkplRaJkhpgG4CSFLL/3MhqxPijtdNiq76HhQ2SpBNXRYqnWB/lMAlgcVgWCyeFQt3C7zy2aVvshu\ntS+dNnoQ+XeiX00KVNCrS1WJiFTFABoRkQUuyfSf02BDJxNON0tE600RSpMlzLw8g9JkCbmDuY6v\nvV1wFmsAXLD+HbdLL2XtO2C9nGhNZG3Hy0Zl1cAKqsOjl1qKMtgHG0wA6na+bJWt6AeZy+ZkbNvr\neXG6L6oFrXqlZl4vLVUlIgoD4V04iYi6Rbd0lQyDtsGGm8Cbb72J+EAclRUVROYjSA4lkd2bdRwU\n6qRTpMhrXw/Ojo6NolAsoKJXEKlGsP1T2/HS6ZdwTj9nufRy7KveGlEI77Zp0flOe+1bWHnp11HZ\n/hPP+57N7sbJkzsxPW0uybYwoevHFmpgPe9pn4Pq8NiQmWf9tlIaOlix7oioAQhH50unnR9Fvp+s\nDo8it93peXG7L7I7a7oh63nhhuzPBW6C/yrcpwDLVxBR92MGGhGRA/xAKJ/tMsAbAL4JzD0811Eh\ndr+XiLZilSH2tS9/DWcmziid9Wi3nMg0P4Obb/8lPnrhY573XWbR8aCyVYJo6GDFrkA/0Afgr633\nT5EsniAaQMhcNidq2yLOi9t9UanRg+wmBXb87uarUtafHTY5IKJewiYCREQ9Kuhvig3DwMiBERQn\niqisqODaG9dw/RPXYT7Q9PfStwF8BMADy7fhphC7ZfH+OhOIFWMoTZY8HIkcQV8fK06LiIvYd5HH\nv5hps8syW0XWhNvvhg7t9qW5QP/27R/DSy/9Hc6de8rX8+KGjAYQ7cZWJ8Xy2xG1bRHnxcu+qNro\nwY/nZRAF/YNqgOIUmxwQkWrYRICIiIRxUwNM9n4MDg8ifzl/K6vM+A8GzJdM4Bwak2QuAdhovR2n\nhdhVqUflhtvJoOx993s5kfBlswFkq6hUS9GqQP/XvvaHOHPmW76fFzdE1YBymiUjs2aeyG13el7c\n7MvS/VG10YMfXzYEUdBfpaw/K2xyQES9hjXQiIh6RCc1wERrWE5ZdweA/wjgr4Ho/4xizT1rcNv8\nbXjrfW9hTpuz3tCSwFfbLo+K1KMSqTmLz0ttOKeCqiUmSn3in8v5m93ntZaizH1cut2gzosTooK2\nbmp9yRznorYt4ry035druHathPXrt9nWV1NprPihFrTcb/lntaDlIeTaJ0O7Ug/+17L+DjVl/QUf\n5A7inBARBYkZaEREPUKlGmC2HTdvB/AYcM/d92Dm5RmUp8pYu3ptqxIwjgNfqtSjEsUqi89LbTgn\n6hko3dL5LqiJf7v3DbqWkGoBEVE1oNxmycgc5yK2Leq82O+LAeARGMYf+VZ3TnVOg5bVqnUn6U6o\nmvUXVIdjIqIgMYBGRNQjbINWcL4UUgSnyynrRAW+snuzSFxIQL+oNywR1S8udIoc9dblMiiyA6JW\ny31vrngDmzb9sbLLicIsiGL5YSAi4OR2uaPMZXOiti3ivNg3l0gDGAXwy+CyvJrWQUsDwJdw5cqr\nuO++X5HeWEAVYWlyQEQkEgNoREQ9QKUaYA3LKa00ZZWJCnypVI9KBJkBUbvstufefg7amit44onv\nKFszK6xYS8hapwEnL1kyMmvmidq2iECc3b5Eo6+gFjxbzk3duW5jHbQ0AOwE8HHMz58NdeDby9//\n3ZKVTETkFLtwEhH1iLZdKAsxlKb86UKZ2ZNB/kreMgBk1VnTMAyMjo2iMFFARa8gUo0gNZTC2OiY\n58msavWe3DBNE/1b+jH76Kzta/pe7MPMyzOejjGzJ4P85XxjjboFS69PmM+hamR2fgy7Tjs/tj+3\n21AqTdj+vsxx3sm2RXfErM8J+vsfw+zsEdvX9fXtwMzM4Z679627+X4JwMdhFXBUoUtmO4ZhYGTk\naRSLp2xr3bX7fRkdjlXr5ExE4SG7CycDaEREPcJt0EqmhoYGGxYbGuiv1rLKWmWE8UNxjcyAaNtt\nF2MoTYoJtvJ6LgREGbRwxMt4yWT2IZ8fXJjgNwpDkMMJkfdRpwFHN8J2/zcHLa9ceRXz82chOvDt\nx3lpbK7xCBaDX8eRSBxyHPwSFcjtNJgnahtEFG6yA2hcwklE1CNUqgHWyXLKME22ZJLVFMGP5b5W\n9dUyezIdLXcK6xeCgDq1hMJwDr2cA5k1zVQhcmzIXpYXdLOMTiwt6P/669/CvfdugKgi+n6fF1HL\nxkU0ORBRA5J1JInID8xAIyLqITKWQooQtiwEFXSSxdeOzOw22/2+pCNxwd1+d1O2QVBZUoZhYOTA\nCIoTRVRWVBCZjyA5lER2bzZ057AV0csdu5msZXmN2+4s60kVorL1/Dwv9b9vVVo2LuL51wuZpkTU\nHpdw2mAAjYioMwxahZ+sgKjM5b5O66vZqY/bbpuIywxatHxPQcHMMOGzrz1ZAcduC3KIOh7Z56X5\ny4bbbruOt96qYm7u27a/4+eycRHBPJUCgkQUHAbQbDCARkREtEhkUCDQ7DaL+mpWWVJrImtx9u9+\nH6b5mWWbCeNEHPA/S6rTYCb1Bn/rq4UryCEq8C3zvNh92QA8DOBvW7ynuFp3rYioAck6kkRUJzuA\ndpvoDRIREZH/RE4K6jXqRsdGUSg2Zbd9tbPOp07rq9WPpyGYl1oM5uFCGfjBl4Dr2wA07k+1uh2F\nwiHkBMV+/MpYqtcSyuX8ec/iRLF2Ti1UN1RRKBaQAwNoQVApS07UfpimiUplNZzUDFPl2NuJRqM4\nffr5hcD3oabAt7Pgmezz0ljrbHGbwBCAo7DuINp5rbulWu17Yw1I62BeuxqQIrZBROQEmwgQERHR\nMtFoFLmDOZQmS5h5eQalyRJyB3MdZUJpmobIfKRVrXxE5iMNk5yRAyO14NnG6tI618AmAMlp4PZR\nq3dyVbzbioxGB2740TBAdrMIcifMxfWdUKVZhmidFtGXfV6KxVMLmWfNdgP4MoAXl7y3uOYabsaz\niMYVsptfyMDnK1H4MIBGRERELQnt8Oeye2hxomhZiw0AsKkKrCpY/MHihNPLBKWe9Za/nEc5Vcbs\no7Mop8rIX8ljcHiwKwIaXoKZJI+fHQSDnLSHMcjhhtf7RdZ5aZ3dFgXwPFav/hJisWH09e1ALDaM\ndPpMxzUX3Y5nEZ1yw9Jtt9sD5UTdjgE0IiIi8k12bxaJCwnoF/WlcxzoF2v11cZGx2691kmWFO6o\noDkKpGkv4K67VnieoNhlvVU3VDG9cRqjY1ZZb+HjNphJ8jQus1scdNXqdkxP78Lo6DMdbV+VSXtY\nghxL+RFwlHVe2me33Ym1az/gOXvOjtvxXF8Km06f8RzME7EN2fwMlBORHGwiQERERL5y0z20XdMB\n/OmHgB9fRr04mqa9gJUr/wCVSq6pqLfz7pxeGh0EzUt9JJnNIsidIIrIB9Wx1u9mGV5YNS5JDiWR\n3ZuVto/d1Pm00/Esog6eirX0uq0LLZGK2IXTBgNoREQUNip+oA9au3OS2ZNB/krechmnflHHRy98\nDFffWHNrwnnXXTrOnv0CqtVPL3+9gwmKaZro39KP2UdnbV/T92IfZl6eCfxaipjkuwlmkhyyOwiq\nPGlX8ZloG1i+pCNxwZ/AsvCuygI6hTrFjpj2uq0LLZGKZAfQuISTiIhIoqCL0auu3QSq3ZLP745P\nNCw/unp13jJQANS7c55quz9+1gbz+kWmqDptMppFkDvBFZF3dk/IZHdMQX7Br8ISbuFdlX1c2tit\nzSI65abbKhGpiwE0IiIiSXqhGL0s9UlENBrF6ROnkV6XRqwYQ9+LfYgVY0ivSzdkgtQbBoiYoMiu\nDSYiqCpjkt9rE1qVBFNEHlBp0q5KnbZWjUuqG6ooTFg1LlFbp51C3er2ZhFeMLBI1B0YQCMiIpJE\nhUyGMLELLAFwlCUlaoLiptGBl2MUEVTtZJIvKliiQtClWwRXRF6NSbsqxdWdNC6p6JVQj30/rnUY\nm0X4gYFFovBjAI2IiEiSbsxk6ESrSafTwFK7yZ+ICYrTrDcvRARVvUzyRS0l5pJkOWQuswvDpF12\nF1In6nXH/FzCHQZegoWyxnOYA5cAA4tE3YBNBIiIiCQIUzF6mZwWus/sySB/OV8LLDXRL+pIr0sj\ndzDn6P1EF8wWWdBbVIfPttspxFCaqm1HVFF0FYqr94owF5H3Iqji6oZhYGTkaRSLp1CprEYkMoc1\nH7yGf3pw0rZxidNnUZhZnZdkciuy2d2exkon41n0vgQtDF1oicKMXThtMIBGRESqcxPkUIHojnhu\nAi6iAkv191VxgiIiqFq/Ru26ky6d5IsKToraDvlj6f2s6j1R388gujYuBhafXGiyUHtAadq3sPKe\nX0dl+08an1uv1pZwd3ug2O686PpxJBKHfA24qrQvMqjYhZYo7NiFk4iIKKRkF6MXQeaSPKfLFUXX\nHfK7YHYzu/30ujzMqrj6zWur8OC5Bx3VaRO1lJhLkp0J8svplnUEA7wnWgmqTpvdslHT/Axuvv2X\n+OiFjwlfwh0GKiynVXFfZGDwjCh8GEAjIiKSRGYxehFkdwl1GnCRWXfIrwmK00Ck26CqXXH15577\nFMxr9+KJtU+0nOSLCk52U3F1GfuoQgdJUXUEgxBEnbZi8dRCVtNypvkZXH1jTdvGJZ1S8X5pdV6q\n1e0oFE715L4QEQEMoBEREUkjsxi9CDK7hLoNuIQhW8+Om0Ck26BqqwyM8+d/DyvnP9Ryki8qOBn2\n4uoyMy1V6SCpStdfL0Ehv4urm6aJSmU1Wj2gKpVVUpbYqRBstePmvPTSvhAR1TGARkREJFE0GkXu\nYE56JoMXMpfkuQ24qJ6t14qbwIXboKrTDIxWk3xRwcmwBjllZ1qqsswsyCW2nQYoZXYhtRLUslFV\ngq12gjovqu+L5bszcEfUkxhAIyIi8olK2Tl+LMlzE3BRPVuvFbeBC6dBVVEZGKKCk2ENcsrOzFJh\nmVmQS2xFBSj9rl0YxLJRVYKtrQRxXsKwL4Da2YNE5A8G0IiIiHqQH0vy3AZcVM7Ws9Np4KLV+RWV\ngSEqOBnWIKfMzCy/l5mJblAhgowApR9fNvi9bBRQI9jaThDnJQz7onr2IBH5gwE0IiKiHmSapvQl\neZ0EXFTK1mtFduBCVAaGqOBk2IKcsjOz/Fhm5jTrJagltkfGj7QMUB45cUTK+3bK72WjqgRb2/H7\nvIRlX8KQPUhE8mlhXb+tadoAgMnJyUkMDAwEvTtERETKMwwDIwdGUJwoorKighU3V+D6tet45+F3\nahNgDbUMsVdrGWKis4pkFORWQWZPBvkrecsggn5RR3pdGrmDOU/brmc9TE/vWjJxM6Hrx5BIPOv7\nJDKM4gNxlFNl67iFCcQKMZSmSp63n8nsQz4/uHB9Gun6UaTTZ/DlL+/zNPYXr/+TC5lL9et/HInE\noYbrX19KOb1x2pf7Gajd09EH78Lcf7DPvln936Iwzl1V/t734/kUjw+hXB6H3WCMxbahVJrwvP3m\nZ3xkPoLkUBLZvVnP116l53aQ+9L+2g2jVBr3e7dcU+l6EskwNTWFzZs3A8Bm0zSnRG+fGWhEREQ9\nwKpO0eu/8jp+/PEf4/1/+37cf+R+6UvyuvVDu8zaYCplYDQLy5ewsjOz7JaZadrzeP/7fx+HD3/H\nc70kN1kvQSyx1TQN771TaZmB+d47lVDc+37so8yaXrKaZah07YJsGBDmjqCs3UYkDjPQiIiIekBm\nTwb5y/lanaIm9SypL//Rl5WaLIWJYRgYHRtFYaKAil5BpBpBaiiFsdGxrsriMwwDIyNPo1g8hUpl\nNSKROSSTW5HN7g48mGd3XvzIzDIMA6Ojz6BQOIVKZRVWrLiG69d/jHfe+cOmzMHlmWOtdJL14sdY\nMU0T0bUbMDf8GvCgxTLOczpWn7gfxpuv8tkCuRmlTp7xXjNhSX72oCxusliJugEz0IiIiKhjTgqp\nc4LrnejaYF6aDsimWhFtwzCQ2ZNBfCCO/i39iA/EkdmTWbYffmRmNXeQ3LHjE3jnnT9CtfppeK2X\n1GnWix9jRdM03LP6PqCYAM41ZmDinA4UE7hn9X18tiyQmVEqq1lGWJMt3Gp3nKp1BHWKtduIxGIG\nGhERUZczTRP9W/ox++is7Wv6XuzDzMsznOgGSEb9IpGc1PrK5fb7si+2WWWXdCQutM4qU6PWlbN6\nSWHIeslk9uErX/k5mCtfAlYVgDsqwHsR4N0UtJufwO/+7lnfxkXYiBqLop/xqj+LRHGTURvWepTd\nUrutlwSdaR52zEAjIiKijsjuFEmdk1W/SKRi8dTCEqDlqtXtKBRO+bYvIwdGasGzjdWlSRWobqhi\neuM0RsdGbX/Xj2WNouolhSHrJZvdjZ/92f8MvbId+PEl4PIM8ONL0Cvb8bM/+3WMjT0V9C4qS9RY\nFPmMD8OzSAS3GbV+1qMU2Yk1zLXbegnr1IUHA2hEREQ9QHYh9TBRcbLQSUColW6diMlariaCpmmI\nRObQKpoRicw5CmbYNSjQ9aNIJJ5VIji1PLDw2LLAgttxoeI9qjLTNIU942U9i1TjZWlj81LtUmkc\nudx+IcEzGQEUkc8ikke18gh+CetzngE0IiKiHiCzU2QYOK2XFRSRASEZx6rSRMw0TVRWVFrF8lDR\nK9I/nLfavqjMMZW7sC5lFVgYG3sKIwdGHI9D1e/RTsgYi80BlyPf/Ee8/zv3dPyMVzk4LUL9WnSa\nUSvyWSczgBKGLNZu4fU+76U6dd3wnGcNNCIioh7hV6fIIHjqwuigXpYfRNYvknmsKtVAiw/EUU6V\n7cr6IFaIoTRVEv6+TmtDyaqXFJbaOG7Hoer3qBcy64jZdVbUtG/h7g9/AdEP6vjpbT91/Yzv1nqZ\nzbXObrvtOt56q4q5uW/b/k5f3w7MzBz25ThlPlvDWrstLER0pu6VOnV+PedZA42IiIiEEN0pMmhO\nv8lUfUmSyPpFMo9VpeWEQSxJdlMbSlbmWFiCFm7Hoer3qFuy64jZZayY5mfwzpWvY8cnfsPTM74b\n62VaZXe99toE5uZuQoWMWkBufcmwZLGGkYjMQdXKIzjhOdOuS57zDKARERH1oDBNgKy4maCGYUmS\nqICQzGNVaSIWxJJktx/+ZdZL8pOXyZLbcej09SpNIluRPVGUGXBx+iwKzbWwCTYCQwCOWv6O6KWN\nrc6VHwGUbnkWqUbE0kuVyiO0IqJGXxg+iznBABoRERGFjtMJqir1stoRERDy41hVmYhFo1GcPnEa\n6XVpxIox9L3Yh1gxhvS6tLTlfp18+A968uNWJ3Vq3I7Dtq+/Cbz51puhqpkjc6LYOuBiANiPmZk3\nPE9yWz2LHjz/IG5cfV+oOgXaBxt3A/gygBchI6PWacDB7wBK2J5FKhMVyFa9Tp2wTLsQfBZzggE0\nIiIiCh2nE9SwLEkSERDy+1jdbEfGh2LRS5LbZol0yYf/djpdfuh2HLZ8/Q0A3wTmHp6TshRSBtlj\nxT7gYgDYCeDjmJ//B8+F6O2eRU+sfQLmtXvx3HOfCk2nwNbBxiiA57F69ZeEZ9S6DTioHkCh5URm\nDqpUHsGKsEy7EHwWc4IBNCIiIgoVtxPUIOpleSEiIKTSsfrZbcvrh25XWSJd8uG/3YROxPJDt+PQ\n9vXfAzAIYBNCUzPHj7FiHXB5GsCTAD6NTjv5WT2LIj/9IM6f3xOqToHts7vuxNq1HxCeUes24KB6\nAIWWE5k5qFJ5BCvCMu0U+nzSCQbQiIiIKFTcTlCDqJfVKa+Ta1WOVXYRdWH76CZLJMQf/t0EM0Us\nP3Q7Du1ej0sANlq/h8o1c2SPFeuAyykA4uui1Z9FMuuuyeQ0u0tk8NvtuZIRQOmGbFjVicwcVKU8\nQjOhmXaKfD7pFANoREREFDpuJqhB1MsKiirHGoZuW66zREL64d9NMFPU8kO349Dq9fcX7sfq960O\n5bJZ2WOlOeCybl0KK1b8BLIK0YexU2Cd39ldXs+ViACKiELvYRPkmJM1tlTKZBaeaafA55NOaSo+\n6JzQNG0AwOTk5CQGBgaC3h0iIiLyUT0oML1xupYto6E2QX21NkFt9WHMNE2lPqDKJOJYvWwjPhBH\nOVW2nkOaQKwYQ2my1NF+dSoeH0K5PI7FnTQb/jsWG0apNN7wO4ZhYHRsFIWJAip6BZFqBKmhFMZG\nx5T98J/Zk0H+cr4WzGyiX9SRXpdG7mDu1s/aXrtCDKUpd9fO7Riqv17GvvjFz7FimibWr9/WNJ4b\nXoFYbBtKpQnP77H8fhG7fZkMw8Do6DMoFE6hUlmFSORdpFJbMTb2lJT7NohzVc+orX0p8Ajqfynq\n+nEkEoeUWAooimEYGBl5GsXiKVQqqxGJzCGZ3Ipsdrfvx+j32ApCJrMP+fzgwpdNjXT9KNLpM8jl\n9rverqzPYlNTU9i8eTMAbDZNc0r09pmBRkRERKHTyTeZYQ6euf3is6PaYD51YQzCYpbIdeD2DHB3\nHPhwf+3ft2cAXLfPEhHYuMAPbpdkylh+6HYc1l8vY1/8Gnd+jhVN06QXog9zoXu/l8cFca5EFHoP\nAxEdIUVSdemlSL2QaecGM9CIiIgo9Lo5q8wwDIwcGEFxoojKigoi8xEkh5LI7s1K+ZBum913SUfi\nQuvsvrowZA7df/+n8PqP3gJS08ADi8eJ8zpQTOC+D/wMXnvtb0I9tkzTRP+Wfsw+Omv7mr4X+zDz\n8sytY+wku1M0Ufvi9z0UhMUMpF1LgigmdP0YEolnhXWWlLX9bhLEuWqf9bY8ozaMZGVDUWthyrST\nnYHGABoRERGRokQEs9xyu+TPdhtX8paZT063IdvP/dst+MeN3wc2WXwW/icN9/zdBxH9wPukBlz8\nCM55CWaqtFS1030J4h4KiuxJbpgm0UHz81yZpon+/scwO3vE9jV9fTswM3M4tF8G1PVKoFBlqn+p\nxACaDQbQiIiIqNuJCGa5JaJ+mUpZTHbu//n78fqO15cf5w0A3wTwcQAPQHjAxe9sqE6DmSpNlrzs\nSxD3kApkXzeVxoXqfAmUh7hGnVO9FCgk71gDjYiIiKhHua1f1amgujD6zTRNzN82b32c3wMwCGAT\nhHcQddMRU5ROO0KqNBH1si9+30OqkH3dVBoXqrB7LvpxrmTVXVMp2UZkR8hepdL1DCsG0IiIiIig\n3gfLIIrxa5qGyHyk1fwEkfmI45b1qhbcb3mcrwPYaP17nQZcRg6M1LLyNlaFB+fsqB7MlCkMDS0o\n3AzDQCazD/H4EPr7H0M8PoRMZp/vxexFFnr365i83HdhbmYRFFXGaLdgAI2IiIh6VifdJmUTGcxy\nQ4UujH6wPE4TwEpIC7gElQ2lcjDTDS9daP28hxiIcyfs50uljpDRaBSnTz+PdPoMYrFh9PXtQCw2\njHT6jKumBbKPqdNgjqyOkN1KpTHaLRhAIyIiop4UxHI6t2QEs9rpdMlfWFgeJwC8CykBF1WyoVQM\nZrbS6YRb9j2kchBeRd2UDTMy8jSmp59c0mkTADRUq9sxPb0Lo6PP+Lo/0WgUudx+lErjmJk5jFJp\nHLncfleBcifH5PUZJSKYIypQ2CtUG6PdgE0EiIiIqCeFobh4UMX4VerCKJPVcd51x104u+GslA6i\nXjpi9rL6hLs2AXwE9RtA148jkTjkaMIs8x7qpQ6fIoi4nirpxo6Q9sdkAPgTrFjxAu69dwMikTkk\nk1uRze6+dc3aNUvIZPYhnx9cCOY00vWjSKfPIJfb72p/2cyitW4co+2wiQARERGRBJ0up/PjS8ig\n6ld1y5K/dqyO87vHvistAy+IjMIwE5E9IfMeCqKmXZh1UzaMaZqoVFajVUpppbIqVMtU7Y/JALAT\nwC9ifv5sQ+bYli078Nu7fttRBmaxeGohcLpctbodhcIp1/vM4Jm9bhyjKmAGGhEREfUc0zTRv6Uf\ns4/O2r6m78U+zLw80/AB3TAMjBwYQXGiiMqKCiLzESSHksjuzfoSXOK37f7oJAOv1TXyM6OwG8aK\njOyJarUKXReTQ9A2o7AYQ2mSGYV13ZYN0/54tqFUmvB7tzpifUz7UGtN3Jw5ZgDRjwKp12qNV1pk\nYJqmif7+xzA7e8T2vfv6dmBm5nDon1sq6cYx2g4z0IiIiIgE81JcXIWaad02sbD7IjfoL3jdZuA5\nrYMlO6Owm+pLicyeWHpe7rvvV4ScF5k17YIe/1612u9uzIbpxo6Q1sd0CoBF5tjtI0ByBngAbTMw\nNU1DJDKHVn/pRiJzXfd3XNC6cYwGjQE0IiIi6klul9NxuZYYdsGmH/7wh0oWY283oXMSWF0aFJC1\nPLbbuq2JmnDLOi+iO3yGtRmB0/3uxgBKN3aEXH5MJgCbwOeqIvCA8zIIDOY08iNY3I1jNGgMoBER\nEVFPctttstOaaWQfbPrKzFew/ufXK90R1Y5tYPUjVbzyk1fQ91CfbWBBZLCgm+pL1Ymm7IH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kShcVnzqBrLmm3Pi03twsV6T0+iWn3k1s91/TgSiUNKFcH3wu55FvbnnFsq1IazE8ZrsWQJp5Qa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6wjSpIkSZIkqZZUuUCLiI7AEOByYHfgZWBMRGywkkuaA18AVwLTKlh6DtBqia8tq5qtPqyo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\nJEmSVIH/D1cIA+Pu20KLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f902e2ddc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o', label='baseline')\n",
    "plt.plot(bn_solver.loss_history, 'o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.train_acc_history, '-o', label='batchnorm')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(solver.val_acc_history, '-o', label='baseline')\n",
    "plt.plot(bn_solver.val_acc_history, '-o', label='batchnorm')\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers = {}\n",
    "solvers = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=True)\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, use_batchnorm=False)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VL1ac9rXb8+zVz7Ki3wqqVaxmNUH0RSDFlYK2OlJKedMkTCmllCqE4nfG03VW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\nYgR8+CF8/DFERPz3PvldEblJSAhcuAB790LlymanMce7oe9y8OxBOs3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udVy69wlzZLkI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wauuw5+/NE9V3LgQFeT8+WXiY4scYYPf8RLwAKDmwAIfn8nvv9+CBdfPIZXX3UJ19tvux87Z892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\nvBfQDFBYq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CKn5VJeF+BHVV0ZmBSr2I2JlmhfBwCqukNVTwEaAn1E5IhYxW9MUcXiGvCW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XlQ9EhRC3ZaLfRAq6FeSlFS9l+3sjMnIL4Pzu6A4NYUqpX5RSsRgBbJ1SKjbdbRfw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dcqjlJKATOBdVrr34s5tT5wuMCxw0A1pVRFrfU5s2IUQghf0a4d/Oc/xkLOY8bAzJlWRySE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\nwkRERERERKJISZiIiIiIiEgU/X+TL88D4+z6UQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8fcc309da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(10, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Question:\n",
    "Describe the results of this experiment, and try to give a reason why the experiment gave the results that it did."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Answer:\n"
   ]
  }
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